Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna Biocenter (VBC), 1030 Vienna, Austria.
Mol Cells. 2019 Feb 28;42(2):104-112. doi: 10.14348/molcells.2019.0006. Epub 2019 Feb 13.
Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease.
通过谱系追踪跟踪单个细胞及其后代的命运已被广泛用于研究各种生物学过程,包括胚胎发育、组织稳态更新以及再生和疾病中的干细胞功能。传统的谱系追踪涉及使用染料或核苷类似物对细胞进行标记,或使用荧光和/或比色蛋白报告基因进行遗传标记。这两种方法都是基于成像的方法,在发育生物学和成人干细胞生物学领域发挥了至关重要的作用。然而,基于成像的谱系追踪方法受到其可扩展性和潜在命运转变的分子信息缺乏的限制。最近,计算生物学方法已与各种追踪方法相结合,以克服这些限制,从而提供更高阶的可扩展性和丰富的分子信息。在这篇综述中,我们将从基于单细胞 RNA 测序的谱系分析开始,介绍这些新的计算方法,介绍到 DNA 条形码或遗传标记分析。这些新方法与传统的基于成像的方法相辅相成,使我们能够在脊椎动物,特别是人类的发育和疾病过程中研究大量细胞类型的谱系关系。